Update README.md

README.md

@@ -1,3 +1,98 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- KingNish/reasoning-base-20k
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen2.5-1.5B
+---
+
+## Uses
+
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        
+        model_name = "/root/app/Reason/checkpoints"
+        
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype="auto",
+            device_map="auto"
+        )
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        
+        from typing import List, Dict
+        def new_apply_chat_template(history:List[Dict[str, str]], add_reasoning_generation_prompt:bool=True, add_assistant_generation_prompt:bool=False):
+          if add_reasoning_generation_prompt:
+            return "".join([f"<|im_start|>{i['role']}\n{i['content']}<|im_end|>\n" for i in history]) + "<|im_start|><|reasoning|>\n"
+          if add_assistant_generation_prompt:
+            return "".join([f"<|im_start|>{i['role']}\n{i['content']}<|im_end|>\n" for i in history]) + "<|im_start|>assistant\n"
+          
+        
+        from IPython.display import Markdown, display
+        device = "cuda"
+        history = []
+        history.append({"role": "system", "content": "You are a helpful assistant"})
+        while True:
+            question = input('User：' + '\n')
+            print(question)
+            print('\n')
+            history.append({"role": "user", "content": question})
+        
+            input_text = new_apply_chat_template(
+                    history,
+                    add_reasoning_generation_prompt=True
+                )
+            model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
+        
+            if model_inputs.input_ids.size()[1]>32000:
+                break
+        
+            generated_ids = model.generate(
+                model_inputs.input_ids,
+                max_new_tokens=3000
+            )
+        
+            if len(generated_ids)>32000:
+                break
+        
+            generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
+        
+            reasoning_response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+            history.append({"role": "<|reasoning|>", "content": reasoning_response})
+            print('reasoning:\n')
+            #print(response)
+            display(Markdown(reasoning_response))
+            print("------------")
+            print('\n')
+        
+            input_text = new_apply_chat_template(
+                    history,
+                    add_assistant_generation_prompt=True
+                )
+            model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
+        
+            if model_inputs.input_ids.size()[1]>32000:
+                break
+        
+            generated_ids = model.generate(
+                model_inputs.input_ids,
+                max_new_tokens=3000
+            )
+        
+            if len(generated_ids)>32000:
+                break
+        
+            generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
+        
+            assistant_response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+            history.append({"role": "assistant", "content": assistant_response})
+            print('assistant:\n')
+            display(Markdown(assistant_response))
+            print("------------")
+        
+        print("超过模型字数上线，已退出")
+
+
+